Systems and methods for meeting a service level at a probable minimum cost

ABSTRACT

An inventory of service parts may be managed by assessing a company&#39;s capability, optimizing the inventory and implementing a management program based on the assessment and optimization. Optimizing target stock levels for the inventory of parts may be accomplished by calculating an inventory baseline for understanding information about the currently held inventory; developing a service strategy for a set of segments; quantifying a service level for each of the segments; analyzing the segments and their service levels for identifying at least one logistically distinct business; assigning each of the segments to a “best-fit” planning model for indicating each segment&#39;s deployment, replenishment, forecasting and review characteristics; identifying a probability distribution function for estimating a demand process of each of the segments; and calculating a target stock level for each segment.

RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.13/550,372, filed Jul. 16, 2012, titled “MANAGING AN INVENTORY OFSERVICE PARTS,” which is a division of U.S. Application Ser. No.10/862,935, filed Jun. 8 , 2004, titled “MANAGING AN INVENTORY OFSERVICE PARTS” which claims the benefit of U.S. Provisional ApplicationNo. 60/577,547, filed Jun. 7, 2004, titled “MANAGING AN INVENTORY OFSERVICE PARTS,” each of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

This invention relates to inventory systems and specifically to planningand deploying inventory systems for service parts used to service andrepair equipment.

Many companies regard post-sale servicing of their products as littlemore than a distraction. From this perspective, service is only anexpense. However, analysis shows that post-sales service can be asignificant revenue generator when properly planned and managed. Forexample, in the highly competitive jet engine business, manufacturershave realized that the value of servicing a product over its life canexceed the original sales price by as much as five times.

The tasks faced by post-sales business groups vary greatly by industryand by customer type. Personal computer manufacturers may have a largeclient base but only a relatively small number of parts to keep on handto service perhaps 30 models of PCs. In contrast, manufacturers ofconstruction or land moving equipment may have a smaller client base butmay need to service such a wide range of complicated machinery thatperhaps 500,000 replacement service parts need to be available to therepair technicians. Tracking and planning for the half million parts isa very challenging task.

Unfortunately, those managing the inventory may not be well qualified.For example, certain OEMs allow their dealers to control partinventories. The planners at these dealerships may treat all partsequally, applying the same forecasting, stocking, lot sizing andreviewing policies—regardless of the demand, supply and profitcharacteristics of the individual parts. For example, planners may makeno distinction between an item with a high-volume demand, stable orderpatterns and quick replenishment lead times, and another item thatrarely fails in the field, is sourced from one supplier and has asix-month lead time. As a result, these dealers may keep excessinventory aging on shelves while lacking the specific parts needed bytheir service technicians.

In an attempt at a solution, some post-sales business units haveimplemented software to help their inventory management needs. However,current transactional and advanced planning software fails to identifyand integrate the detailed root causes of inventory performance. Forexample, demand for service parts typically exhibits random, orstochastic demand patterns; this uncertainty must be included indeveloping deployment and replenishment strategies. Also, enterpriseresource planning systems currently available may help OEMs to meet only40%-60% of their high-volume post-sales needs. This leaves a 60%-40% gapthat has been difficult to fix.

What is needed is a way to close the gap between the inventory ofservice parts and the need for the parts. What is needed is a method toprioritize the management of a parts inventory to optimize the process,thereby having the proper mix of inventory to meet agreed upon servicelevels without overstocking on inventory. What is also needed is a wayto perform such management without requiring planners to have anadvanced degree in statistics or operations research. What is needed isa methodology that is cost-effective as well as scientifically basedrather than only being based on rules of thumb or ‘guestimates’. What isneeded is a way to determine the drivers of inventory levels for serviceparts and to control these drivers. In addition, what is needed is asystematic and easy-to-understand methodology and toolset for that willallow the average user to conduct advanced optimization techniques ontheir service parts inventories.

There are several other factors and issues typically associated withmanaging service parts inventories. First, supplier performance forservice parts is typically very poor; replenishment lead times typicallyrange from 6-18 months, and line fill rates typically are less than 50%.Next, service parts distribution networks are typically fragmented withlots of individual locations: central depots, field depots, customerdepots, and mobile stock. The large number of forward deployedinventories makes inventory visibility difficult, thereby making it verydifficult to develop and implement stable deployment and replenishmentstrategies. Next, many service parts are also considered repairables,which are parts that can be fixed when they fail, instead of disposed.Repair operations require reverse flows (from customer to repairdepots), forecasting of repairs, and disposition decisions, all whichfurther add complexity to managing service parts.

BRIEF SUMMARY OF THE INVENTION

In one aspect of the present invention, there is a method for optimizingtarget stock levels for the inventory of parts, such as those used byasset operators and equipment manufacturers to service their equipment.Under this method, a service strategy may be developed for a set ofsegments and a service level may be quantified for each segment. Thesegments and their service levels may be analyzed for identifying atleast one logistically distinct business. Each of the segments may beassigned to a “best-fit” planning model for indicating each segment'sdeployment, replenishment, forecasting and review characteristics. Aprobability distribution function may be identified for estimating ademand process of each of the segments. In one embodiment, identifyingthe probability distribution function may include calculating a targetstock level for each of the segments. Such target stock levels may becalculated to likely meeting a service level at a desired costobjective.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of one embodiment of the invention divided intofive steps.

FIG. 2 illustrates how sampling may represent an ABCD population.

FIG. 3 is a flowchart of the one embodiment of the invention's samplingmethodology.

FIG. 4 is an illustration of pipelines identifying a logisticallydistinct business.

FIG. 5 is a diagram of a planning model continuum.

FIG. 6 shows one approach to calculating the distribution function ascontemplated by the present invention.

FIG. 7 demonstrates how distribution functions may offer insight.

FIG. 8 illustrates a structured analysis for the stocking decision.

DETAILED DESCRIPTION OF THE INVENTION

1. Calculating the Baseline Inventory

FIG. 1 shows a flowchart of a general steps in one embodiment of theinvention, which includes calculating the current baseline inventory,developing a strategy and a series of segments, assigning each segmentto a planning model, matching the demand for service parts with adistribution function and then calculating optimized target stocklevels.

The present invention's process may begin by calculating an inventorybaseline 110 for understanding information about the currently heldinventory. While gathering this baseline is known in the art and thereare various techniques that can be used, some of representative tasksare to gather initial inventory data, build the inventory baseline modeland validate, modify/customize data requests to operating environment,determine data sampling strategy, identify data sources within the ITinfrastructure, and submit detailed data requests.

A goal of the inventory baseline is to understand the present inventory:such as by answering what is on-hand, where is it, what are itscharacteristics and how well is it currently operating. While it is nottechnically difficult to generate the baseline, it can be a difficulttask to handle. In many industries, service parts are not tracked oncethey are distributed to the repair technicians. In such a case, theinventory of service parts may be considered an operating expense. Thus,once perhaps 30 to 50 percent of the inventory is sent ahead tosatellite depots or to individual technicians, that inventory becomesinvisible and outside of the equation.

Since tracking down the inventory piece by piece may be undulychallenging, software may be used to create statistically valid samplesof the inventory to approximate the inventory baseline. This can createa representation of the inventory while minimizing collection time andeffort. In some environments, one may extract transactional data fromeach network echelon and location to model the entire supply chain. Togenerate the sample, a planner may determine the total parts populationto be sampled and then determine if a sampling strategy is needed (sincesometimes analyzing the entire parts population may not be toodifficult). If a sample is needed, then the planner may create astatistically valid sample from the population and identify randomizedstock keeping units (“SKUs”) to be in the sample. Then data for thoseSKUs may be extracted from various data sources and the statisticalanalysis performed.

FIGS. 2 and 3 illustrate one novel way to create a proper sample. Thisapproach reflects design decisions made about the present invention'soptimization approach. FIG. 2 shows a parts population as a pie chart205 that can be represented by sample 210. In FIG. 2, the partspopulation is segmented into four classes A through D. Such aclassification may be referred to as ABCD. In such a classification, theparts assigned to class A make up 80% of cumulative dollar volume for acompany 215, class B has parts representing the parts such that class Aand B make up 95% of cumulative dollar volume 220. Similarly, class Cincludes the parts such that classes A, B and C make up 100% ofcumulative dollar value 225. Class D may include those parts that makeup 0% dollar volume 230.

One skilled in the art will recognize that the parts population may bedivided in various ways without departing from the scope of the presentinvention. For example, some in the art commonly divide populations intothree classes, known as ABC. One might also divide the classes based ona criteria other than collar volume. Thus various numbers of classes andvarious class criteria may be chosen. Regardless of how theclassifications are chosen, a sample 210 may statistically represent theprofile of the parts in the population 205.

One may use various methods to generate the sample. FIG. 3 illustratesone approach that may be used in one embodiment of the presentinvention. While FIG. 3 assumes that the population and sample have foursegments, namely A, B, C and D, one skilled in the art will recognizethat the method may be used for other segmentations.

At step 310, the total population of the parts to be included [N] may bedetermined. The scope of the analysis may be defined at this point, suchas the number of locations or echelons, the number of internal vs.external sites, active vs. inactive parts, etc. At this point, one maychoose to distinguish SKUs from raw product numbers.

At step 320, whether or not a sampling strategy is needed is determined.This decision may be driven by the analysis tool to be used. Forexample, Excel may only process approximately 65,000 records while adatabase tool may process many more than that number. If the population[N] is small enough, sampling may not be needed.

If a sampling strategy is not needed (step 324), then the entire partspopulation [N] may be modeled or analyzed without the use of a samplepopulation. Otherwise, the process of FIG. 3 may continue in order togenerate a statistically valid and randomized sample (326). At step 330,ABCD unit volume segmentation may be conducted on the entire partspopulation to determine the population count for each category, N_(A),N_(B), N_(C) and N_(D). Such segmentation may have already been done.Or, if necessary, the category populations may be estimated by applyingpercentages to the overall [N].

At step 340, the sampling error, or level of precision to be used in theanalysis [e] may be determined. In some circumstances, the samplingerror chosen may be 90% (0.10) or 95% (0.05). Of course, other samplingerrors may be used. In the analysis, [e] may be the probability measurethat states how much the sample characteristicssuch as the mean andstandard deviation, for example may deviate from the population if [N]had been analyzed instead of [n].

At step 350, the sample sizes for each of the segments are calculated.For example, ABCD segments would have (n_(A), n_(B), n_(C) and n_(D)).One way to calculate these sample sizes is with the formula:n _(x) =N _(x)/1+N _(x)(e)²

where e may be 0.05, 0.10 or another preferred value and where xrepresents a segment, such as A, B, C, or D as discussed above. Thus, iffour segments are used, then the formula is applied four times to derivea sample size for each of the segments. One skilled in the art will beaware that this formula is a simplified version of more advancedsampling techniques and that other formulas may also be used.

At step 360, the product numbers in each segment are sorted or rankedbased on some chosen criterion measure. For example, unit cost and itemname are two possible criteria. Once the product numbers are sorted,then each part number may be assigned its ranking number. For example,the first product number in segment A may be assigned to 1. If this isnot feasible to system limitations or otherwise, then steps 360, 370 and380 may be skipped.

At step 370, a random number generator is used to randomly select theparts to include in the sample from each segment. For example, if Excelis being used as the sampling tool, then the formula “=RANDBETWEEN(1,333)” may be used to generate the random numbers (where the currentsegment has 333 elements, for example). If using Excel, one may wish touse the Excel Analysis Toolpak add-in. The quantity of random numbersneeded to be generate matches the values of n_(A), n_(B), n_(C) andn_(D).

At step 380, the randomly selected parts are identified and their datais extracted. For example, if N_(A) is 2,000 and n_(A) is 333, then onemay generate 333 random numbers between 1 and 2,000. Then one may matchthose random numbers against the sorted, numbered A items. These maythen become the sampled parts for that category.

As another way to minimize the effort of calculating the baseline, thepresent invention offers a statistical analysis tool with data requestspreconfigured for different environments. For example, data requests forwholesale distribution, retail aftermarket, airline MRO, telecommmaintenance, high-tech spares, dealer channels, public utility/energy,military logistics, fixed asset maintenance and plant operations may allbe installed. The invention contains data requests that collect the datain a method that facilitates the optimization approach in the invention.

At the end of the baseline inventory procedure, some of the deliverablesmay be the inventory baseline model, completed data request templates,and a data sampling and management strategy.

2. Developing a Strategy and Defining Segments

Developing a service strategy 120 for a plurality of segments is anotherstep of the present invention. While it is shown in FIG. 1 after step110, actually it may be done before, during or after that step. Broadlyspeaking, this step involves viewing the parts inventory from a top-downview and understand a strategy for what kinds of service levels thecompany wants to offer to its customers. One of goals of this step is toselect a segmentation criteria that is based on business requirementsand that may create meaningful and unique segments that can be analyzedfor operational insights.

Two examples of segmentation criteria are a supply-focused segmentationand a demand-focused segmentation. In a supply-focused segmentationapproach, the chosen segments may be based on criteria such as: supplierlead time variation and duration, supplier delivery performance,standard cost of part, replenishment frequency, economic lot size,supplier relationship type, and part lifecycle phase. In contrast, in ademand-focused segmentation, the segments may be based on: criticalityof customer demand, degree of demand perishability, part profitabilityor contribution margin, economic importance of customer, strategicimportance of customer, demand variability, and interrelation with othersegment businesses.

Results from segmentation may be used to find “pipelines” within thecustomer's business to understand how to manage a segment in a morefocused manner. FIG. 4 shows one example of pipelines and howlogistically distinct businesses (“LDBs”) may be identified within thebroader parts supply chain. In a complex supply chain that is tightlymanaged, there may only be a single LDB. Other supply chains mayindicate the presence of more than one LDB. In FIG. 4, a “churn andburn” LDB is indicated.

LDBs are critical to structure the customer service strategy of aservice business for three reasons. First, construction of LDBs allowsthe business to identify the unique physical flows that exist in anydistribution network. For example, the LDB modeling may demonstrate thata specific grouping of customers also exhibit a specific orderingprofile, such that they are driving the majority of the logisticsactivity in the network. Second, LDB's also allow the business toidentify the unique service and supply chain requirements associatedwith that LDB. For example, LDB modeling will identify the lead time,fill rate, packaging, delivery, etc. requirements that are associatedwith a logical grouping of customers. Last, LDBs are important becausethey determine the value component associated with a logical grouping ofcustomers and parts. For example, LDB modeling may show that a group ofcustomers drive the majority of sales, are the least profitableaccounts, have the highest service requirements, but are alsostrategically important to the business and therefore require newselling and operating capabilities to manage.

Segmentation may also be described from different viewpoints. Forexample, high-level customer segmentation models may be built. Partsegmentation models may able be constructed. For other companies, it maymake sense to identify the service levels by channel, customer grouping,SKU, etc.

Once the segments are identified, a planner may use empirical data toquantify a service level for each of the segments. Assigning the servicelevel attempts to balance the cost of holding or carrying the inventoryfor the segment against the cost of a stock-out in which a neededservice part must be ordered. Service levels may be defined implicitlyor explicitly. With the implicit method, one uses a calculation to implythe optimum service level as a function of stock-out costs and carryingcosts. Such an implicit service level may be a best-fit if the valuesfor the input parameters to the function are available, if the businesshas not existing customer service strategy, or if the business does notunderstand cost-to-serve concepts. When the service level is implicitlydetermined, the planning planner considers costs and service todetermine the optimum balance for a customer or LDB. Presently, mostcompanies use the implicit method.

The explicit service level approach uses management expertise to set theacceptable minimum number of planned stock-outs. While in the past acompany's sales force may have instructed the parts inventory planner tomaintain perhaps a 98% fill rate, there was no tie back to the companyin terms of cost. The explicit service level approaches provide suchinformation.

Such an explicit approach may be valid if the business management iscapable of assigning discrete, differentiated service levels tocustomers. Three such explicit levels are: cycle service level, fillrate level, and ready rate level. In the cycle service level measure, aspecified probability of no stock-outs per replenishment cycle iscalculated. This is generally known as an availability measure and caninclude the probability of periods having zero demand. This cycleservice level may be a best fit with numerous periods of zero demand.

The fill rate service level is a specific fraction of demand to besatisfied routinely from the shelf. This is the most common measure andassumes no backorders or lost sales. It may be a best fit forhigher-volume parts or for distribution systems where replenishment leadtimes are fixed or very costly to change.

The ready rate service level is a specific fraction of time during whichnet stock is positive. It may be the least common measure, but hasapplication in emergency environments. It is complex to use to determineoptimal inventory policy, but may be applicable for end-of-lifeproducts.

One embodiment of the present invention combines these approaches andmay leverage the implicit method for critical and high-value items whileusing one of the explicit methods for less-critical or less profitableparts.

3. Assigning Planning Models

Referring again to FIG. 1, the next step may be to assign each of thesegments to a “best-fit” planning model 130, which is a decision drivenby the requirements and rules that define each segmentation class. Sucha planning model describes the deployment, replenishment, forecastingand review parameters for the segment. FIG. 5 is a diagram of a planningmodel continuum that illustrates the variation in the planning modelsfrom the most basic (on the left) to the most advanced approaches (onthe right).

This continuum indicates that rather than attempting to offer thehighest level of service for all parts, one can be more surgical bychoosing plans along the continuum that best fit each segment. Forexample, for low churn items, having the deployment plan be a rule ofthumb (such as a time-based criteria with 10 days coverage), may beadequate since reordering can be done quickly. Thus, a replenishmentstrategy for such a plan may be to order every 5 days. Forecasting maybe accomplished using historical sales (with a weighted moving average).The review strategy may be for the planners to review such rules ofthumb once a quarter.

At the right end of the spectrum of FIG. 5, some segments may need acomplex plan. While the cost of this type of plan may be greater, it maybe justified due to the critical nature of the parts, the highprofitability of the parts, etc. This type of planning model has beensupported by commercially available software since around the year 2000.Some of the many vendors offering software to support such complex plansare: SAP, Servigistics, Finmatica, i2, Manugistics, MCA Solutions,Baxter Planning Systems, and Xelus.

One of the goals of this step of choosing a best-fit planning model foreach segment is to focus planners on what is important rather thanhaving them try to manage all service parts equally. Planners may nowspend the proper time and effort on the proper segments for an optimizedplanning approach for deployment, replenishment, forecasting and review.

4. Determining a Probability Distribution Function

Once the best-fit planning model is assigned, the fourth step in theprocess may be to identify a probability distribution function (“PDF”)140. In this step, one may use a range of statistical tests to identify(i.e., “fit”) the demand process to the most likely probabilitydistribution that represents the demand. This can be a difficultmathematical procedure, but generally one may collect demand data andthen find the probability distribution function that underlies the partand model that function to gain insight from it.

As one would expect, if demand is normally distributed (such as a bellcurve), then managing the stock for a part is generally easy.Unfortunately, part demand is not always normally distributed. Priorpart systems for inventory management did one of two things. The firsttype of prior part systems skipped this step of determining thedistribution. Rather than understand the complex function describing thedistribution, a planner calculated target stock levels for the serviceparts using rules of thumb or other rough approaches.

The second type of prior part systems used a back door approach toattempt to generate a number associated with the probabilitydistribution function. In such prior part techniques, one usedhistorical demand data for the services parts and used that data tosimulate the stocking locations. The simulation would be run to find thefirst pass fill rate (“FPFR”). Once the FPFR was known, one would runthe simulation again on an iterative basis, slowly (and clumsily)backing into the number hopefully associated with the distributionfunction. This technique was used without understanding the functionitself. Such systems had many disadvantages. In addition to notrevealing a probability distribution for the demand, distributions withprobabilistic functions, stochastic functions or randomized functionswere generally missed. By directly calculating the probabilitydistribution function, the present invention overcomes the shortcomingsof such prior art systems.

Another prime disadvantage of using this back door approach is that itis not repeatable or consistent. For example, if this trial-by-errormethod is used and the optimum stocking level is determined, it is mostlikely by random chance; the dynamic nature of service parts supply anddemand will make this same result impossible to recreate in the nextplanning cycle. Likewise, if the back door approach is used and over- orunder-service conditions results, there is no ability to determine aroot cause of the event because there was no formality in determining itin the first place.

FIG. 6 shows one approach to calculating the distribution function ascontemplated by the present invention. First, a planner may collectdemand data 610. Preferably, data is collected for a part segment or LDBto represent multiple, similar SKUs. In one embodiment, monthly demanddata for one to three years may be collected. To minimize the dataneeds, a manageable set of candidate demand processes may be selectedand used as proxies across other, similar demand processes.

Second, the data may be analyzed for insights 620. Histograms may begenerated to visualize the shape and skewness of the distribution. Thedata may also be analyzed for auto-correlation errors and independencemay be assessed.

Third, distribution fitting tests may be performed on the data 630. Inthis step, statistical software may be used to perform fitness tests. Inone embodiment of the present invention, 22 predefined probabilitydistributions may be compared with the fitness tests. Such tests maygenerate a relative score (out of 100, for example) based on thedistribution parameters.

Fourth, comparative analytics may be run to select the distributionfunction 640. Graphical overlays between the histogram and theprobability density function may be performed. Fitness tests (such asthe Chi Square test, the Kolmogorov-Smirnov test, and theAnderson-Darling test) may be run against specific probabilitydistribution functions. Based on the results of these and other tests,the final distribution may be chosen for use in the deploymentalgorithm. While the tests involved in FIG. 6 are well known in the art,such traditional planning approaches for service parts fail because theyapply a standard distribution to a non-normally distributed part. Thepresent invention overcomes this deficiency.

Using the present invention's method for step 140, a planner generatesthe function associated to demand and can therefore gain insight fromit. For example, if the function is stochastic, the planner wouldrealize that the part number would need to be managed more closely andrigorously. To illustrate the insights that may be gained, refer to FIG.7. The top distribution function is a normal bell-curve 710. In such adistribution, one may calculate the reorder point and the safety stock730 that should be kept on hand in inventory. However, if an inventorypart is represented by a Poisson function 720, then the mean andvariance the are same value, the reorder point is at a differentlocation and the safety stock that should be kept on hand 740 is muchgreater.

The approach described here in step 140 may be less costly to apply thanthe brute force, back door approach of the prior art systems and thefindings may be more accurate, which translates to cost savings forinventory management.

5. Calculating Target Stock Levels

As a final step shown in FIG. 1's general view of the invention, thetarget stock levels (“TSL”) for the segments are calculated 150 based onthe deployment algorithm. This step of the process may answer questionsfor deployment and replenishment. For example, for deployment, the TSLsmay answer how much inventory is required to ensure an adequate level ofservice. For replenishment, the TSLs may answer when inventory should beorder or moved, and by how much.

To calculate the TSLs at this step of the process, specific algorithmpolicies may be applied that may perform well over a wide range ofdemand values and types. Such policies are valuable because in thetraditional planning approach to parts inventory management, a z-scoreis calculated for an area under the distribution curve. The shaded area(see FIG. 5) represents the demand that the planning meets while theunshaded portion represents the amount of inventory that will bestock-outs. Since current systems cannot calculate non-normaldistributions readily or accurately, a z-score may not be accurate. Thepolicies in this step offered by the present invention convert theprobability distribution function into a planning language that isusable by planners. For example, the planner may plug in the monthlydemand for a SKU in units as well as the variation in units to calculatean inventory plan for the part. The accuracy of the present inventionmay double the savings of inventory costs over the prior art.

Two such algorithms that may be used are (S-1,S) and (s,Q), where “S”stands for inventory level and the first parameter is the inventorylevel and the second parameter is the order quantity. In this case,(S-1, S) is a stocking model where the system will order one unit (S)when one unit is used and falls below the level of S. (S-1,S) is alsocalled “issue one, replace one.” This algorithm may perform well for lowdemand and sporadic demand parts, such as a satellite hub for telecommnetworks and parts closets for a utility service. On the other hand, the(s,Q) policy may perform well for moderate demand and high volume demandparts where it is desirable that replenishment lot sizes may vary. Forexample, dealer parts inventory and plant maintenance stockrooms maymake the (s,Q) appropriate. These two models explicitly account fordemand variability, supply variability, and service requirements. Thepolicies are easy to understand, easy to implement, and may beautomated.

Once the TSLs are calculated using such models as (S-1, S) or (s,Q), aplanner may also need to use a structured analysis to make a stockingdecision (i.e., to stock or not to stock) for individual SKUs. FIG. 8illustrates one example of a structured analysis for the stockingdecision, based on an airplane company.

The foregoing description addresses embodiments encompassing theprinciples of the present invention. The embodiments may be changed,modified and/or implemented using various types of arrangements. Thoseskilled in the art will readily recognize various modifications andchanges that may be made to the invention without strictly following theexemplary embodiments and applications illustrated and described herein,and without departing from the scope of the invention, which is setforth in the following claims.

What is claimed is:
 1. A method comprising: identifying, using at leastone hardware processor and based on receiving baseline inventoryinformation, a service strategy for assigning service levels to aplurality of segments, the baseline inventory information reducing acollection time, and usage of the at least one hardware processor,associated with collecting information associated with a currentinventory of service parts, each service level being associated with theservice parts, each service level being associated with one of theplurality of segments, each of the plurality of segments beingassociated with a different classification of the service parts,identifying the service strategy including: selecting a segmentationcriteria to identify the plurality of segments, and utilizing empiricaldata to quantify the service levels for the plurality of segments;receiving, using the at least one hardware processor, informationindicating an assignment of best-fit planning models to the plurality ofsegments, each best-fit planning model being associated with one of theplurality of segments; identifying, using the at least one hardwareprocessor and based on receiving the information indicating theassignment of the best-fit planning models to the plurality of segments,a probability distribution function that matches a demand associatedwith the plurality of segments, the probability distribution functionrepresenting the demand; performing a simulation, using the at least onehardware processor and based on the best-fit planning models, to testthe probability distribution function against historical usage data andto determine the service levels, the service levels being determinedbased on the service strategy; adjusting, using the at least onehardware processor, a deployment algorithm based on performing thesimulation, the deployment algorithm being used to calculate targetstock levels for the plurality of segments; selectively repeating, usingthe at least one hardware processor, identifying the probabilitydistribution function, performing the simulation, and adjusting thedeployment algorithm until the target stock levels cause the servicelevels to be satisfied; providing for display, using the at least onehardware processor, the target stock levels for the plurality ofsegments when the target stock levels cause the service levels to besatisfied; receiving, using the at least one hardware processor,information associated with a demand for a particular service part, ofthe service parts, and a variation in the demand for the particularservice part; determining, using the at least one hardware processor, aninventory plan for the particular service part based on the target stocklevels, the demand for the particular service part, and the variation inthe demand for the particular service part, the inventory planincreasing savings, associated with inventory costs, for a supplier ofthe particular service part; and providing for display, using the atleast one hardware processor, the inventory plan for the particularservice part.
 2. The method of claim 1, where identifying theprobability distribution function comprises: identifying a firstpossible probability distribution function using a fitting test;identifying a second possible probability distribution function usingthe fitting test; and choosing to use the first possible probabilitydistribution function or the second possible probability distributionfunction based on a deviation from expected outcomes.
 3. The method ofclaim 1, where at least one of the service levels comprises a cycleservice level.
 4. The method of claim 1, where at least one of theservice levels comprises a fill rate service level.
 5. The method ofclaim 1, where at least one of the service levels comprises a ready rateservice level.
 6. The method of claim 1, further comprising: selectivelyrepeating identifying the probability distribution function, performingthe simulation, and adjusting the deployment algorithm until an optimumservice level, as a function of stock-out costs and carrying costs, issatisfied for each of the service levels.
 7. The method of claim 1,further comprising: accessing demand data associated with a plurality ofstock keeping units; generating a histogram to represent a distributionof demand based on the demand data; performing a distribution fittingtest based on the demand data; and selecting the probabilitydistribution function based on the histogram and the distributionfitting test.
 8. The method of claim 7, where the plurality of stockkeeping units represent a logistically distinct business.
 9. The methodof claim 1, further comprising: classifying the probability distributionfunction according to a class of statistical distribution.
 10. Themethod of claim 9, where adjusting the deployment algorithm based onperforming the simulation comprises: adjusting the deployment algorithmbased on the class of statistical distribution.
 11. A computer-basedsystem comprising: a memory to store instructions; and a hardwareprocessor to execute the instructions to: identify, based on receivingbaseline inventory information, a service strategy for assigning servicelevels to a plurality of segments, the baseline inventory informationreducing a collection time, and usage of the hardware processor,associated with collecting information associated with a currentinventory of service parts, each service level being associated with theservice parts, each service level being associated with one of theplurality of segments, each of the plurality of segments beingassociated with a different classification of the service parts, theservice strategy being identified based on selecting a segmentationcriteria to identify the plurality of segments, and utilizing empiricaldata to quantify the service levels for the plurality of segments;receive information indicating an assignment of best-fit planning modelsto the plurality of segments, each best-fit planning model beingassociated with one of the plurality of segments; identify, based onreceiving the information indicating the assignment of the best-fitplanning models to the plurality of segments, a probability distributionfunction that matches a demand associated with the plurality ofsegments, the probability distribution function representing the demand;perform a simulation, based on the best-fit planning models, to test theprobability distribution function against historical usage data and todetermine the service levels, the service levels being determined basedon the service strategy; adjust a deployment algorithm based onperforming the simulation, the deployment algorithm being used tocalculate target stock levels for the plurality of segments; selectivelyidentify the probability distribution function, perform the simulation,and adjust the deployment algorithm repetitively until the target stocklevels cause the service levels to be satisfied; provide, for display,the target stock levels for the plurality of segments when the targetstock levels cause the service levels to be satisfied; receive,information associated with a demand for a particular service part, ofthe service parts, and a variation in the demand for the particularservice part; determine an inventory plan for the particular servicepart based on the target stock levels, the demand for the particularservice part, and the variation in the demand for the particular servicepart, the inventory plan increasing savings, associated with inventorycosts, for a supplier of the particular service part; and provide, fordisplay, the inventory plan for the particular service part.
 12. Thecomputer-based system of claim 11, where the hardware processor, whenidentifying the probability distribution function, is to: identify afirst possible probability distribution function using a fitting test;identify a second possible probability distribution function using thefitting test; and choose to use the first possible probabilitydistribution function or the second possible probability distributionfunction based on a deviation from expected outcomes.
 13. Thecomputer-based system of claim 11, where each of the service levelscomprises a cycle service level.
 14. The computer-based system of claim11, where each of the service levels comprises a fill rate servicelevel.
 15. The computer-based system of claim 11, where each of theservice levels comprises a ready rate service level.
 16. Thecomputer-based system of claim 11, where the hardware processor isfurther to: selectively identify the probability distribution function,perform the simulation, and adjust the deployment algorithm repetitivelyuntil an optimum service level, as a function of stock-out costs andcarrying costs, is satisfied for each of the service levels.
 17. Thecomputer-based system of claim 11, where the hardware processor isfurther to: access demand data associated with a plurality of stockkeeping units; generate a histogram to represent a distribution ofdemand based on the demand data; perform a distribution fitting testbased on the demand data; and select the probability distributionfunction based on the histogram and the distribution fitting test. 18.The computer-based system of claim 17, where the plurality of stockkeeping units represent a logistically distinct business.
 19. Thecomputer-based system of claim 11, where the hardware processor isfurther to: classify the probability distribution function according toa class of statistical distribution.
 20. The computer-based system ofclaim 19, where the hardware processor, when adjusting the deploymentalgorithm based on performing the simulation, is to: adjust thedeployment algorithm based on the class of statistical distribution.